Overview

Dataset statistics

Number of variables23
Number of observations10127
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory527.2 B

Variable types

NUM17
CAT6

Warnings

Avg_Open_To_Buy is highly correlated with Credit_LimitHigh correlation
Credit_Limit is highly correlated with Avg_Open_To_BuyHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 is highly correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1High correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 is highly correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2High correlation
CLIENTNUM has unique values Unique
Dependent_count has 904 (8.9%) zeros Zeros
Contacts_Count_12_mon has 399 (3.9%) zeros Zeros
Total_Revolving_Bal has 2470 (24.4%) zeros Zeros
Avg_Utilization_Ratio has 2470 (24.4%) zeros Zeros

Reproduction

Analysis started2020-11-29 20:43:46.013729
Analysis finished2020-11-29 20:44:26.311049
Duration40.3 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

CLIENTNUM
Real number (ℝ≥0)

UNIQUE

Distinct10127
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean739177606.3
Minimum708082083
Maximum828343083
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:26.417656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum708082083
5-th percentile709120390.5
Q1713036770.5
median717926358
Q3773143533
95-th percentile814212033
Maximum828343083
Range120261000
Interquartile range (IQR)60106762.5

Descriptive statistics

Standard deviation36903783.45
Coefficient of variation (CV)0.04992546194
Kurtosis-0.6156397044
Mean739177606.3
Median Absolute Deviation (MAD)6347700
Skewness0.9956010103
Sum7.485651619e+12
Variance1.361889233e+15
MonotocityNot monotonic
2020-11-29T21:44:26.601435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7800975331< 0.1%
 
7200490831< 0.1%
 
7173767581< 0.1%
 
7205983081< 0.1%
 
7199306581< 0.1%
 
7168054081< 0.1%
 
8199979831< 0.1%
 
7781919331< 0.1%
 
8241656581< 0.1%
 
7712207581< 0.1%
 
7115154331< 0.1%
 
8203461331< 0.1%
 
7189435081< 0.1%
 
7201456831< 0.1%
 
7796892331< 0.1%
 
8247637831< 0.1%
 
7151239831< 0.1%
 
7193981581< 0.1%
 
7876211581< 0.1%
 
8229646831< 0.1%
 
7195117831< 0.1%
 
8072266831< 0.1%
 
7152796831< 0.1%
 
7797650581< 0.1%
 
7941604081< 0.1%
 
Other values (10102)1010299.8%
 
ValueCountFrequency (%) 
7080820831< 0.1%
 
7080832831< 0.1%
 
7080845581< 0.1%
 
7080854581< 0.1%
 
7080869581< 0.1%
 
7080951331< 0.1%
 
7080981331< 0.1%
 
7080991831< 0.1%
 
7081005331< 0.1%
 
7081036081< 0.1%
 
ValueCountFrequency (%) 
8283430831< 0.1%
 
8282989081< 0.1%
 
8282949331< 0.1%
 
8282918581< 0.1%
 
8282883331< 0.1%
 
8282858581< 0.1%
 
8282817331< 0.1%
 
8282361331< 0.1%
 
8282274331< 0.1%
 
8282155081< 0.1%
 

Attrition_Flag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Existing Customer
8500 
Attrited Customer
1627 
ValueCountFrequency (%) 
Existing Customer850083.9%
 
Attrited Customer162716.1%
 
2020-11-29T21:44:26.742562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:26.812310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:26.900352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length17
Median length17
Mean length17
Min length17

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
t2350813.7%
 
i1862710.8%
 
s1862710.8%
 
e117546.8%
 
r117546.8%
 
101275.9%
 
C101275.9%
 
u101275.9%
 
o101275.9%
 
m101275.9%
 
E85004.9%
 
x85004.9%
 
n85004.9%
 
g85004.9%
 
A16270.9%
 
d16270.9%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter14177882.4%
 
Uppercase Letter2025411.8%
 
Space Separator101275.9%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1012750.0%
 
E850042.0%
 
A16278.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
t2350816.6%
 
i1862713.1%
 
s1862713.1%
 
e117548.3%
 
r117548.3%
 
u101277.1%
 
o101277.1%
 
m101277.1%
 
x85006.0%
 
n85006.0%
 
g85006.0%
 
d16271.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
10127100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin16203294.1%
 
Common101275.9%
 

Most frequent Latin characters

ValueCountFrequency (%) 
t2350814.5%
 
i1862711.5%
 
s1862711.5%
 
e117547.3%
 
r117547.3%
 
C101276.2%
 
u101276.2%
 
o101276.2%
 
m101276.2%
 
E85005.2%
 
x85005.2%
 
n85005.2%
 
g85005.2%
 
A16271.0%
 
d16271.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
10127100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII172159100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
t2350813.7%
 
i1862710.8%
 
s1862710.8%
 
e117546.8%
 
r117546.8%
 
101275.9%
 
C101275.9%
 
u101275.9%
 
o101275.9%
 
m101275.9%
 
E85004.9%
 
x85004.9%
 
n85004.9%
 
g85004.9%
 
A16270.9%
 
d16270.9%
 

Customer_Age
Real number (ℝ≥0)

Distinct45
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3259603
Minimum26
Maximum73
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:27.020996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile33
Q141
median46
Q352
95-th percentile60
Maximum73
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.016814033
Coefficient of variation (CV)0.1730523011
Kurtosis-0.2886199153
Mean46.3259603
Median Absolute Deviation (MAD)6
Skewness-0.03360501632
Sum469143
Variance64.26930723
MonotocityNot monotonic
2020-11-29T21:44:27.173058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
445004.9%
 
494954.9%
 
464904.8%
 
454864.8%
 
474794.7%
 
434734.7%
 
484724.7%
 
504524.5%
 
424264.2%
 
513983.9%
 
533873.8%
 
413793.7%
 
523763.7%
 
403613.6%
 
393333.3%
 
543073.0%
 
383033.0%
 
552792.8%
 
562622.6%
 
372602.6%
 
572232.2%
 
362212.2%
 
351841.8%
 
591571.6%
 
581571.6%
 
Other values (20)126712.5%
 
ValueCountFrequency (%) 
26780.8%
 
27320.3%
 
28290.3%
 
29560.6%
 
30700.7%
 
31910.9%
 
321061.0%
 
331271.3%
 
341461.4%
 
351841.8%
 
ValueCountFrequency (%) 
731< 0.1%
 
701< 0.1%
 
682< 0.1%
 
674< 0.1%
 
662< 0.1%
 
651011.0%
 
64430.4%
 
63650.6%
 
62930.9%
 
61930.9%
 

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
F
5358 
M
4769 
ValueCountFrequency (%) 
F535852.9%
 
M476947.1%
 
2020-11-29T21:44:27.303312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:27.373729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:27.463636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
F535852.9%
 
M476947.1%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter10127100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
F535852.9%
 
M476947.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin10127100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
F535852.9%
 
M476947.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII10127100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
F535852.9%
 
M476947.1%
 

Dependent_count
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.346203219
Minimum0
Maximum5
Zeros904
Zeros (%)8.9%
Memory size79.2 KiB
2020-11-29T21:44:27.562354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.298908349
Coefficient of variation (CV)0.5536214162
Kurtosis-0.6830166531
Mean2.346203219
Median Absolute Deviation (MAD)1
Skewness-0.02082553562
Sum23760
Variance1.687162899
MonotocityNot monotonic
2020-11-29T21:44:27.677922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
3273227.0%
 
2265526.2%
 
1183818.1%
 
4157415.5%
 
09048.9%
 
54244.2%
 
ValueCountFrequency (%) 
09048.9%
 
1183818.1%
 
2265526.2%
 
3273227.0%
 
4157415.5%
 
54244.2%
 
ValueCountFrequency (%) 
54244.2%
 
4157415.5%
 
3273227.0%
 
2265526.2%
 
1183818.1%
 
09048.9%
 

Education_Level
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Graduate
3128 
High School
2013 
Unknown
1519 
Uneducated
1487 
College
1013 
Other values (2)
967 
ValueCountFrequency (%) 
Graduate312830.9%
 
High School201319.9%
 
Unknown151915.0%
 
Uneducated148714.7%
 
College101310.0%
 
Post-Graduate5165.1%
 
Doctorate4514.5%
 
2020-11-29T21:44:27.818628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:27.907684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:28.037728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length8
Mean length8.939271255
Min length7

Overview of Unicode Properties

Unique unicode characters25
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
a922610.2%
 
e909510.0%
 
o79768.8%
 
d66187.3%
 
t65497.2%
 
n60446.7%
 
u51315.7%
 
r40954.5%
 
l40394.5%
 
h40264.4%
 
c39514.4%
 
G36444.0%
 
g30263.3%
 
U30063.3%
 
H20132.2%
 
i20132.2%
 
20132.2%
 
S20132.2%
 
k15191.7%
 
w15191.7%
 
C10131.1%
 
P5160.6%
 
s5160.6%
 
-5160.6%
 
D4510.5%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter7534383.2%
 
Uppercase Letter1265614.0%
 
Space Separator20132.2%
 
Dash Punctuation5160.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
G364428.8%
 
U300623.8%
 
H201315.9%
 
S201315.9%
 
C10138.0%
 
P5164.1%
 
D4513.6%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
a922612.2%
 
e909512.1%
 
o797610.6%
 
d66188.8%
 
t65498.7%
 
n60448.0%
 
u51316.8%
 
r40955.4%
 
l40395.4%
 
h40265.3%
 
c39515.2%
 
g30264.0%
 
i20132.7%
 
k15192.0%
 
w15192.0%
 
s5160.7%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
2013100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-516100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin8799997.2%
 
Common25292.8%
 

Most frequent Latin characters

ValueCountFrequency (%) 
a922610.5%
 
e909510.3%
 
o79769.1%
 
d66187.5%
 
t65497.4%
 
n60446.9%
 
u51315.8%
 
r40954.7%
 
l40394.6%
 
h40264.6%
 
c39514.5%
 
G36444.1%
 
g30263.4%
 
U30063.4%
 
H20132.3%
 
i20132.3%
 
S20132.3%
 
k15191.7%
 
w15191.7%
 
C10131.2%
 
P5160.6%
 
s5160.6%
 
D4510.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
201379.6%
 
-51620.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII90528100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
a922610.2%
 
e909510.0%
 
o79768.8%
 
d66187.3%
 
t65497.2%
 
n60446.7%
 
u51315.7%
 
r40954.5%
 
l40394.5%
 
h40264.4%
 
c39514.4%
 
G36444.0%
 
g30263.3%
 
U30063.3%
 
H20132.2%
 
i20132.2%
 
20132.2%
 
S20132.2%
 
k15191.7%
 
w15191.7%
 
C10131.1%
 
P5160.6%
 
s5160.6%
 
-5160.6%
 
D4510.5%
 

Marital_Status
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Married
4687 
Single
3943 
Unknown
749 
Divorced
748 
ValueCountFrequency (%) 
Married468746.3%
 
Single394338.9%
 
Unknown7497.4%
 
Divorced7487.4%
 
2020-11-29T21:44:28.178987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:28.260856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:28.371519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length7
Mean length6.684506764
Min length6

Overview of Unicode Properties

Unique unicode characters17
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
r1012215.0%
 
i937813.9%
 
e937813.9%
 
n61909.1%
 
d54358.0%
 
M46876.9%
 
a46876.9%
 
S39435.8%
 
g39435.8%
 
l39435.8%
 
o14972.2%
 
U7491.1%
 
k7491.1%
 
w7491.1%
 
D7481.1%
 
v7481.1%
 
c7481.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter5756785.0%
 
Uppercase Letter1012715.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
M468746.3%
 
S394338.9%
 
U7497.4%
 
D7487.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
r1012217.6%
 
i937816.3%
 
e937816.3%
 
n619010.8%
 
d54359.4%
 
a46878.1%
 
g39436.8%
 
l39436.8%
 
o14972.6%
 
k7491.3%
 
w7491.3%
 
v7481.3%
 
c7481.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin67694100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
r1012215.0%
 
i937813.9%
 
e937813.9%
 
n61909.1%
 
d54358.0%
 
M46876.9%
 
a46876.9%
 
S39435.8%
 
g39435.8%
 
l39435.8%
 
o14972.2%
 
U7491.1%
 
k7491.1%
 
w7491.1%
 
D7481.1%
 
v7481.1%
 
c7481.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII67694100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
r1012215.0%
 
i937813.9%
 
e937813.9%
 
n61909.1%
 
d54358.0%
 
M46876.9%
 
a46876.9%
 
S39435.8%
 
g39435.8%
 
l39435.8%
 
o14972.2%
 
U7491.1%
 
k7491.1%
 
w7491.1%
 
D7481.1%
 
v7481.1%
 
c7481.1%
 

Income_Category
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Less than $40K
3561 
$40K - $60K
1790 
$80K - $120K
1535 
$60K - $80K
1402 
Unknown
1112 
ValueCountFrequency (%) 
Less than $40K356135.2%
 
$40K - $60K179017.7%
 
$80K - $120K153515.2%
 
$60K - $80K140213.8%
 
Unknown111211.0%
 
$120K +7277.2%
 
2020-11-29T21:44:28.504138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:28.583482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:28.958138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length14
Median length12
Mean length11.4801027
Min length7

Overview of Unicode Properties

Unique unicode characters22
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
1730314.9%
 
$1374211.8%
 
01374211.8%
 
K1374211.8%
 
s71226.1%
 
n68975.9%
 
453514.6%
 
-47274.1%
 
L35613.1%
 
e35613.1%
 
t35613.1%
 
h35613.1%
 
a35613.1%
 
631922.7%
 
829372.5%
 
122621.9%
 
222621.9%
 
U11121.0%
 
k11121.0%
 
o11121.0%
 
w11121.0%
 
+7270.6%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3159927.2%
 
Decimal Number2974625.6%
 
Uppercase Letter1841515.8%
 
Space Separator1730314.9%
 
Currency Symbol1374211.8%
 
Dash Punctuation47274.1%
 
Math Symbol7270.6%
 

Most frequent Currency Symbol characters

ValueCountFrequency (%) 
$13742100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01374246.2%
 
4535118.0%
 
6319210.7%
 
829379.9%
 
122627.6%
 
222627.6%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
K1374274.6%
 
L356119.3%
 
U11126.0%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
17303100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4727100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
s712222.5%
 
n689721.8%
 
e356111.3%
 
t356111.3%
 
h356111.3%
 
a356111.3%
 
k11123.5%
 
o11123.5%
 
w11123.5%
 

Most frequent Math Symbol characters

ValueCountFrequency (%) 
+727100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common6624557.0%
 
Latin5001443.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
1730326.1%
 
$1374220.7%
 
01374220.7%
 
453518.1%
 
-47277.1%
 
631924.8%
 
829374.4%
 
122623.4%
 
222623.4%
 
+7271.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
K1374227.5%
 
s712214.2%
 
n689713.8%
 
L35617.1%
 
e35617.1%
 
t35617.1%
 
h35617.1%
 
a35617.1%
 
U11122.2%
 
k11122.2%
 
o11122.2%
 
w11122.2%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII116259100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
1730314.9%
 
$1374211.8%
 
01374211.8%
 
K1374211.8%
 
s71226.1%
 
n68975.9%
 
453514.6%
 
-47274.1%
 
L35613.1%
 
e35613.1%
 
t35613.1%
 
h35613.1%
 
a35613.1%
 
631922.7%
 
829372.5%
 
122621.9%
 
222621.9%
 
U11121.0%
 
k11121.0%
 
o11121.0%
 
w11121.0%
 
+7270.6%
 

Card_Category
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size79.2 KiB
Blue
9436 
Silver
 
555
Gold
 
116
Platinum
 
20
ValueCountFrequency (%) 
Blue943693.2%
 
Silver5555.5%
 
Gold1161.1%
 
Platinum200.2%
 
2020-11-29T21:44:29.076535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-29T21:44:29.146895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:29.256165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length4
Mean length4.117507653
Min length4

Overview of Unicode Properties

Unique unicode characters16
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
l1012724.3%
 
e999124.0%
 
u945622.7%
 
B943622.6%
 
i5751.4%
 
S5551.3%
 
v5551.3%
 
r5551.3%
 
G1160.3%
 
o1160.3%
 
d1160.3%
 
P20< 0.1%
 
a20< 0.1%
 
t20< 0.1%
 
n20< 0.1%
 
m20< 0.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter3157175.7%
 
Uppercase Letter1012724.3%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
B943693.2%
 
S5555.5%
 
G1161.1%
 
P200.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
l1012732.1%
 
e999131.6%
 
u945630.0%
 
i5751.8%
 
v5551.8%
 
r5551.8%
 
o1160.4%
 
d1160.4%
 
a200.1%
 
t200.1%
 
n200.1%
 
m200.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin41698100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
l1012724.3%
 
e999124.0%
 
u945622.7%
 
B943622.6%
 
i5751.4%
 
S5551.3%
 
v5551.3%
 
r5551.3%
 
G1160.3%
 
o1160.3%
 
d1160.3%
 
P20< 0.1%
 
a20< 0.1%
 
t20< 0.1%
 
n20< 0.1%
 
m20< 0.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII41698100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
l1012724.3%
 
e999124.0%
 
u945622.7%
 
B943622.6%
 
i5751.4%
 
S5551.3%
 
v5551.3%
 
r5551.3%
 
G1160.3%
 
o1160.3%
 
d1160.3%
 
P20< 0.1%
 
a20< 0.1%
 
t20< 0.1%
 
n20< 0.1%
 
m20< 0.1%
 

Months_on_book
Real number (ℝ≥0)

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.9284092
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:29.388419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile22
Q131
median36
Q340
95-th percentile50
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.986416331
Coefficient of variation (CV)0.2222869453
Kurtosis0.4001001202
Mean35.9284092
Median Absolute Deviation (MAD)4
Skewness-0.1065653599
Sum363847
Variance63.78284581
MonotocityNot monotonic
2020-11-29T21:44:29.524846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%) 
36246324.3%
 
373583.5%
 
343533.5%
 
383473.4%
 
393413.4%
 
403333.3%
 
313183.1%
 
353173.1%
 
333053.0%
 
303003.0%
 
412972.9%
 
322892.9%
 
282752.7%
 
432732.7%
 
422712.7%
 
292412.4%
 
442302.3%
 
452272.2%
 
272062.0%
 
461971.9%
 
261861.8%
 
471711.7%
 
251651.6%
 
481621.6%
 
241601.6%
 
Other values (19)134213.3%
 
ValueCountFrequency (%) 
13700.7%
 
14160.2%
 
15340.3%
 
16290.3%
 
17390.4%
 
18580.6%
 
19630.6%
 
20740.7%
 
21830.8%
 
221051.0%
 
ValueCountFrequency (%) 
561031.0%
 
55420.4%
 
54530.5%
 
53780.8%
 
52620.6%
 
51800.8%
 
50960.9%
 
491411.4%
 
481621.6%
 
471711.7%
 

Total_Relationship_Count
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.812580231
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:29.628729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.554407865
Coefficient of variation (CV)0.4077049586
Kurtosis-1.006130507
Mean3.812580231
Median Absolute Deviation (MAD)1
Skewness-0.162452415
Sum38610
Variance2.416183812
MonotocityNot monotonic
2020-11-29T21:44:29.732883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
3230522.8%
 
4191218.9%
 
5189118.7%
 
6186618.4%
 
2124312.3%
 
19109.0%
 
ValueCountFrequency (%) 
19109.0%
 
2124312.3%
 
3230522.8%
 
4191218.9%
 
5189118.7%
 
6186618.4%
 
ValueCountFrequency (%) 
6186618.4%
 
5189118.7%
 
4191218.9%
 
3230522.8%
 
2124312.3%
 
19109.0%
 

Months_Inactive_12_mon
Real number (ℝ≥0)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.341167177
Minimum0
Maximum6
Zeros29
Zeros (%)0.3%
Memory size79.2 KiB
2020-11-29T21:44:29.828772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.010622399
Coefficient of variation (CV)0.4316745978
Kurtosis1.098522614
Mean2.341167177
Median Absolute Deviation (MAD)1
Skewness0.633061129
Sum23709
Variance1.021357634
MonotocityNot monotonic
2020-11-29T21:44:29.936950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
3384638.0%
 
2328232.4%
 
1223322.0%
 
44354.3%
 
51781.8%
 
61241.2%
 
0290.3%
 
ValueCountFrequency (%) 
0290.3%
 
1223322.0%
 
2328232.4%
 
3384638.0%
 
44354.3%
 
51781.8%
 
61241.2%
 
ValueCountFrequency (%) 
61241.2%
 
51781.8%
 
44354.3%
 
3384638.0%
 
2328232.4%
 
1223322.0%
 
0290.3%
 

Contacts_Count_12_mon
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.455317468
Minimum0
Maximum6
Zeros399
Zeros (%)3.9%
Memory size79.2 KiB
2020-11-29T21:44:30.039874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.106225143
Coefficient of variation (CV)0.4505426109
Kurtosis0.0008626566254
Mean2.455317468
Median Absolute Deviation (MAD)1
Skewness0.01100562622
Sum24865
Variance1.223734066
MonotocityNot monotonic
2020-11-29T21:44:30.148210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
3338033.4%
 
2322731.9%
 
1149914.8%
 
4139213.7%
 
03993.9%
 
51761.7%
 
6540.5%
 
ValueCountFrequency (%) 
03993.9%
 
1149914.8%
 
2322731.9%
 
3338033.4%
 
4139213.7%
 
51761.7%
 
6540.5%
 
ValueCountFrequency (%) 
6540.5%
 
51761.7%
 
4139213.7%
 
3338033.4%
 
2322731.9%
 
1149914.8%
 
03993.9%
 

Credit_Limit
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6205
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8631.953698
Minimum1438.3
Maximum34516
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:30.285320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1438.3
5-th percentile1438.51
Q12555
median4549
Q311067.5
95-th percentile34516
Maximum34516
Range33077.7
Interquartile range (IQR)8512.5

Descriptive statistics

Standard deviation9088.77665
Coefficient of variation (CV)1.052922313
Kurtosis1.808989336
Mean8631.953698
Median Absolute Deviation (MAD)2593
Skewness1.666725808
Sum87415795.1
Variance82605861
MonotocityNot monotonic
2020-11-29T21:44:30.443525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
345165085.0%
 
1438.35075.0%
 
15987180.2%
 
9959180.2%
 
23981120.1%
 
6224110.1%
 
2490110.1%
 
3735110.1%
 
7469100.1%
 
206980.1%
 
200170.1%
 
280270.1%
 
271270.1%
 
1493870.1%
 
222270.1%
 
160660.1%
 
272160.1%
 
280160.1%
 
318760.1%
 
211160.1%
 
228360.1%
 
291760.1%
 
290060.1%
 
196360.1%
 
263660.1%
 
Other values (6180)891888.1%
 
ValueCountFrequency (%) 
1438.35075.0%
 
14392< 0.1%
 
14401< 0.1%
 
14412< 0.1%
 
14421< 0.1%
 
14433< 0.1%
 
14461< 0.1%
 
14492< 0.1%
 
14512< 0.1%
 
14522< 0.1%
 
ValueCountFrequency (%) 
345165085.0%
 
344961< 0.1%
 
344581< 0.1%
 
344271< 0.1%
 
341981< 0.1%
 
341731< 0.1%
 
341621< 0.1%
 
341401< 0.1%
 
340581< 0.1%
 
340101< 0.1%
 

Total_Revolving_Bal
Real number (ℝ≥0)

ZEROS

Distinct1974
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.814061
Minimum0
Maximum2517
Zeros2470
Zeros (%)24.4%
Memory size79.2 KiB
2020-11-29T21:44:30.594400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1359
median1276
Q31784
95-th percentile2517
Maximum2517
Range2517
Interquartile range (IQR)1425

Descriptive statistics

Standard deviation814.9873352
Coefficient of variation (CV)0.7008750257
Kurtosis-1.145991782
Mean1162.814061
Median Absolute Deviation (MAD)591
Skewness-0.1488372503
Sum11775818
Variance664204.3566
MonotocityNot monotonic
2020-11-29T21:44:30.750045image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0247024.4%
 
25175085.0%
 
1965120.1%
 
1480120.1%
 
1720110.1%
 
1664110.1%
 
1434110.1%
 
1542100.1%
 
1175100.1%
 
1560100.1%
 
1590100.1%
 
1528100.1%
 
1421100.1%
 
1250100.1%
 
1482100.1%
 
1794100.1%
 
787100.1%
 
1650100.1%
 
1176100.1%
 
111590.1%
 
154090.1%
 
186490.1%
 
130090.1%
 
143090.1%
 
151990.1%
 
Other values (1949)691868.3%
 
ValueCountFrequency (%) 
0247024.4%
 
1321< 0.1%
 
1341< 0.1%
 
1451< 0.1%
 
1541< 0.1%
 
1571< 0.1%
 
1592< 0.1%
 
1682< 0.1%
 
1701< 0.1%
 
1861< 0.1%
 
ValueCountFrequency (%) 
25175085.0%
 
25143< 0.1%
 
25131< 0.1%
 
25122< 0.1%
 
25111< 0.1%
 
25092< 0.1%
 
25082< 0.1%
 
25074< 0.1%
 
25061< 0.1%
 
25053< 0.1%
 

Avg_Open_To_Buy
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6813
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7469.139637
Minimum3
Maximum34516
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:30.898108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile480.3
Q11324.5
median3474
Q39859
95-th percentile32183.4
Maximum34516
Range34513
Interquartile range (IQR)8534.5

Descriptive statistics

Standard deviation9090.685324
Coefficient of variation (CV)1.217099394
Kurtosis1.798617296
Mean7469.139637
Median Absolute Deviation (MAD)2665
Skewness1.661696546
Sum75639977.1
Variance82640559.65
MonotocityNot monotonic
2020-11-29T21:44:31.057022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1438.33243.2%
 
34516981.0%
 
31999260.3%
 
78780.1%
 
95370.1%
 
70170.1%
 
46370.1%
 
71370.1%
 
74060.1%
 
93360.1%
 
150760.1%
 
68360.1%
 
80660.1%
 
99060.1%
 
98860.1%
 
78860.1%
 
99760.1%
 
64960.1%
 
162360.1%
 
66460.1%
 
83760.1%
 
112960.1%
 
91360.1%
 
117960.1%
 
103860.1%
 
Other values (6788)954194.2%
 
ValueCountFrequency (%) 
31< 0.1%
 
101< 0.1%
 
142< 0.1%
 
151< 0.1%
 
241< 0.1%
 
281< 0.1%
 
291< 0.1%
 
361< 0.1%
 
392< 0.1%
 
412< 0.1%
 
ValueCountFrequency (%) 
34516981.0%
 
343621< 0.1%
 
343021< 0.1%
 
343001< 0.1%
 
342971< 0.1%
 
342861< 0.1%
 
342381< 0.1%
 
342271< 0.1%
 
341401< 0.1%
 
341191< 0.1%
 

Total_Amt_Chng_Q4_Q1
Real number (ℝ≥0)

Distinct1158
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7599406537
Minimum0
Maximum3.397
Zeros5
Zeros (%)< 0.1%
Memory size79.2 KiB
2020-11-29T21:44:31.213666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.463
Q10.631
median0.736
Q30.859
95-th percentile1.103
Maximum3.397
Range3.397
Interquartile range (IQR)0.228

Descriptive statistics

Standard deviation0.2192067692
Coefficient of variation (CV)0.288452484
Kurtosis9.993501179
Mean0.7599406537
Median Absolute Deviation (MAD)0.114
Skewness1.732063411
Sum7695.919
Variance0.04805160768
MonotocityNot monotonic
2020-11-29T21:44:31.371074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.791360.4%
 
0.743340.3%
 
0.712340.3%
 
0.735330.3%
 
0.718330.3%
 
0.722320.3%
 
0.744320.3%
 
0.699320.3%
 
0.767310.3%
 
0.69310.3%
 
0.631310.3%
 
0.731310.3%
 
0.717310.3%
 
0.703310.3%
 
0.788300.3%
 
0.749300.3%
 
0.693300.3%
 
0.709290.3%
 
0.691290.3%
 
0.644290.3%
 
0.708290.3%
 
0.742290.3%
 
0.725290.3%
 
0.664290.3%
 
0.67290.3%
 
Other values (1133)935392.4%
 
ValueCountFrequency (%) 
05< 0.1%
 
0.011< 0.1%
 
0.0181< 0.1%
 
0.0461< 0.1%
 
0.0612< 0.1%
 
0.0721< 0.1%
 
0.1011< 0.1%
 
0.121< 0.1%
 
0.1531< 0.1%
 
0.1631< 0.1%
 
ValueCountFrequency (%) 
3.3971< 0.1%
 
3.3551< 0.1%
 
2.6751< 0.1%
 
2.5941< 0.1%
 
2.3681< 0.1%
 
2.3571< 0.1%
 
2.3161< 0.1%
 
2.2821< 0.1%
 
2.2751< 0.1%
 
2.2711< 0.1%
 

Total_Trans_Amt
Real number (ℝ≥0)

Distinct5033
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4404.086304
Minimum510
Maximum18484
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:31.524596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum510
5-th percentile1283.3
Q12155.5
median3899
Q34741
95-th percentile14212
Maximum18484
Range17974
Interquartile range (IQR)2585.5

Descriptive statistics

Standard deviation3397.129254
Coefficient of variation (CV)0.7713584656
Kurtosis3.894023406
Mean4404.086304
Median Absolute Deviation (MAD)1308
Skewness2.041003403
Sum44600182
Variance11540487.17
MonotocityNot monotonic
2020-11-29T21:44:31.679776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4253110.1%
 
4509110.1%
 
2229100.1%
 
4518100.1%
 
486990.1%
 
404290.1%
 
431390.1%
 
422090.1%
 
449890.1%
 
403790.1%
 
146880.1%
 
427580.1%
 
431780.1%
 
173180.1%
 
434880.1%
 
407780.1%
 
467480.1%
 
483380.1%
 
135370.1%
 
469770.1%
 
247370.1%
 
390670.1%
 
427770.1%
 
140970.1%
 
439970.1%
 
Other values (5008)991897.9%
 
ValueCountFrequency (%) 
5101< 0.1%
 
5301< 0.1%
 
5631< 0.1%
 
5691< 0.1%
 
5941< 0.1%
 
5961< 0.1%
 
5971< 0.1%
 
6021< 0.1%
 
6151< 0.1%
 
6431< 0.1%
 
ValueCountFrequency (%) 
184841< 0.1%
 
179951< 0.1%
 
177441< 0.1%
 
176341< 0.1%
 
176281< 0.1%
 
174981< 0.1%
 
174371< 0.1%
 
173901< 0.1%
 
173501< 0.1%
 
172581< 0.1%
 

Total_Trans_Ct
Real number (ℝ≥0)

Distinct126
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.85869458
Minimum10
Maximum139
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:31.825160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q145
median67
Q381
95-th percentile105
Maximum139
Range129
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.47257045
Coefficient of variation (CV)0.3619032206
Kurtosis-0.3671632411
Mean64.85869458
Median Absolute Deviation (MAD)17
Skewness0.1536730685
Sum656824
Variance550.9615635
MonotocityNot monotonic
2020-11-29T21:44:31.986313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
812082.1%
 
752032.0%
 
712032.0%
 
822022.0%
 
692022.0%
 
761982.0%
 
771971.9%
 
701931.9%
 
781901.9%
 
741901.9%
 
671861.8%
 
791841.8%
 
731831.8%
 
801731.7%
 
681701.7%
 
831691.7%
 
721681.7%
 
651661.6%
 
661641.6%
 
641581.6%
 
631501.5%
 
851481.5%
 
431471.5%
 
841471.5%
 
371411.4%
 
Other values (101)568756.2%
 
ValueCountFrequency (%) 
104< 0.1%
 
112< 0.1%
 
124< 0.1%
 
135< 0.1%
 
1490.1%
 
15160.2%
 
16130.1%
 
17130.1%
 
18230.2%
 
19110.1%
 
ValueCountFrequency (%) 
1391< 0.1%
 
1381< 0.1%
 
1341< 0.1%
 
1321< 0.1%
 
13160.1%
 
1305< 0.1%
 
12960.1%
 
128100.1%
 
127120.1%
 
126100.1%
 

Total_Ct_Chng_Q4_Q1
Real number (ℝ≥0)

Distinct830
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7122223758
Minimum0
Maximum3.714
Zeros7
Zeros (%)0.1%
Memory size79.2 KiB
2020-11-29T21:44:32.139258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.368
Q10.582
median0.702
Q30.818
95-th percentile1.069
Maximum3.714
Range3.714
Interquartile range (IQR)0.236

Descriptive statistics

Standard deviation0.2380860913
Coefficient of variation (CV)0.3342861716
Kurtosis15.6892929
Mean0.7122223758
Median Absolute Deviation (MAD)0.119
Skewness2.064030568
Sum7212.676
Variance0.05668498689
MonotocityNot monotonic
2020-11-29T21:44:32.308649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.6671711.7%
 
11661.6%
 
0.51611.6%
 
0.751561.5%
 
0.61131.1%
 
0.81011.0%
 
0.714920.9%
 
0.833850.8%
 
0.778690.7%
 
0.625630.6%
 
0.7590.6%
 
0.571570.6%
 
0.857530.5%
 
0.659480.5%
 
0.756470.5%
 
0.556460.5%
 
0.591460.5%
 
0.727460.5%
 
0.711450.4%
 
0.786440.4%
 
0.545440.4%
 
0.875440.4%
 
0.694440.4%
 
0.66430.4%
 
0.722430.4%
 
Other values (805)824181.4%
 
ValueCountFrequency (%) 
070.1%
 
0.0281< 0.1%
 
0.0291< 0.1%
 
0.0381< 0.1%
 
0.0531< 0.1%
 
0.0592< 0.1%
 
0.0621< 0.1%
 
0.0741< 0.1%
 
0.0773< 0.1%
 
0.0913< 0.1%
 
ValueCountFrequency (%) 
3.7141< 0.1%
 
3.5711< 0.1%
 
3.51< 0.1%
 
3.251< 0.1%
 
32< 0.1%
 
2.8751< 0.1%
 
2.751< 0.1%
 
2.5711< 0.1%
 
2.53< 0.1%
 
2.4291< 0.1%
 

Avg_Utilization_Ratio
Real number (ℝ≥0)

ZEROS

Distinct964
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2748935519
Minimum0
Maximum0.999
Zeros2470
Zeros (%)24.4%
Memory size79.2 KiB
2020-11-29T21:44:32.468422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.023
median0.176
Q30.503
95-th percentile0.793
Maximum0.999
Range0.999
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.2756914693
Coefficient of variation (CV)1.002902641
Kurtosis-0.7949719515
Mean0.2748935519
Median Absolute Deviation (MAD)0.176
Skewness0.7180079968
Sum2783.847
Variance0.07600578622
MonotocityNot monotonic
2020-11-29T21:44:32.623195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0247024.4%
 
0.073440.4%
 
0.057330.3%
 
0.048320.3%
 
0.06300.3%
 
0.045290.3%
 
0.061290.3%
 
0.069280.3%
 
0.059280.3%
 
0.053270.3%
 
0.077260.3%
 
0.039260.3%
 
0.07260.3%
 
0.067250.2%
 
0.056240.2%
 
0.066240.2%
 
0.071240.2%
 
0.054240.2%
 
0.079240.2%
 
0.15240.2%
 
0.05230.2%
 
0.042230.2%
 
0.041230.2%
 
0.1230.2%
 
0.068230.2%
 
Other values (939)701569.3%
 
ValueCountFrequency (%) 
0247024.4%
 
0.0041< 0.1%
 
0.0051< 0.1%
 
0.0063< 0.1%
 
0.0071< 0.1%
 
0.0082< 0.1%
 
0.0091< 0.1%
 
0.011< 0.1%
 
0.0111< 0.1%
 
0.0124< 0.1%
 
ValueCountFrequency (%) 
0.9991< 0.1%
 
0.9951< 0.1%
 
0.9941< 0.1%
 
0.9921< 0.1%
 
0.991< 0.1%
 
0.9881< 0.1%
 
0.9871< 0.1%
 
0.9851< 0.1%
 
0.9841< 0.1%
 
0.9834< 0.1%
 
Distinct1704
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.159997464
Minimum7.6642e-06
Maximum0.99958
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:32.773721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.6642e-06
5-th percentile4.2358e-05
Q19.8983e-05
median0.00018146
Q30.0003373
95-th percentile0.99697
Maximum0.99958
Range0.9995723358
Interquartile range (IQR)0.000238317

Descriptive statistics

Standard deviation0.3653010124
Coefficient of variation (CV)2.283167516
Kurtosis1.417535175
Mean0.159997464
Median Absolute Deviation (MAD)0.000110234
Skewness1.848538415
Sum1620.294318
Variance0.1334448296
MonotocityNot monotonic
2020-11-29T21:44:32.933826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.00019864800.8%
 
0.0003139780.8%
 
0.00030251770.8%
 
0.00018665730.7%
 
0.00011382710.7%
 
0.00019143660.7%
 
0.00011811660.7%
 
0.00017987630.6%
 
0.00018145600.6%
 
0.00016883590.6%
 
0.00030516560.6%
 
9.6126e-05560.6%
 
0.00028395560.6%
 
0.00018864520.5%
 
0.00010684510.5%
 
5.5077e-05500.5%
 
5.7151e-05490.5%
 
0.00032275490.5%
 
9.2637e-05490.5%
 
8.6952e-05480.5%
 
0.00017486470.5%
 
0.0001861470.5%
 
0.00011065470.5%
 
0.00017969450.4%
 
0.00010386440.4%
 
Other values (1679)868885.8%
 
ValueCountFrequency (%) 
7.6642e-061< 0.1%
 
7.7559e-061< 0.1%
 
1.0252e-051< 0.1%
 
1.0546e-051< 0.1%
 
1.1536e-051< 0.1%
 
1.4461e-051< 0.1%
 
1.6948e-051< 0.1%
 
1.6949e-051< 0.1%
 
1.7434e-052< 0.1%
 
1.7785e-051< 0.1%
 
ValueCountFrequency (%) 
0.999583< 0.1%
 
0.999541< 0.1%
 
0.999453< 0.1%
 
0.999444< 0.1%
 
0.999431< 0.1%
 
0.999422< 0.1%
 
0.9993960.1%
 
0.999385< 0.1%
 
0.999372< 0.1%
 
0.999361< 0.1%
 
Distinct640
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8400025708
Minimum0.00041998
Maximum0.99999
Zeros0
Zeros (%)0.0%
Memory size79.2 KiB
2020-11-29T21:44:33.088251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.00041998
5-th percentile0.00302546
Q10.99966
median0.99982
Q30.9999
95-th percentile0.99996
Maximum0.99999
Range0.99957002
Interquartile range (IQR)0.00024

Descriptive statistics

Standard deviation0.3653010371
Coefficient of variation (CV)0.4348808561
Kurtosis1.417535174
Mean0.8400025708
Median Absolute Deviation (MAD)0.00011
Skewness-1.848538414
Sum8506.706035
Variance0.1334448477
MonotocityNot monotonic
2020-11-29T21:44:33.742233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.999896316.2%
 
0.999945515.4%
 
0.999814784.7%
 
0.99994524.5%
 
0.999933943.9%
 
0.999883913.9%
 
0.999823733.7%
 
0.99983443.4%
 
0.999913413.4%
 
0.999693223.2%
 
0.999833133.1%
 
0.999842792.8%
 
0.999952652.6%
 
0.999972602.6%
 
0.999922212.2%
 
0.999961831.8%
 
0.999871801.8%
 
0.999721641.6%
 
0.999671591.6%
 
0.999791471.5%
 
0.999851411.4%
 
0.999681401.4%
 
0.999781231.2%
 
0.999711221.2%
 
0.99971121.1%
 
Other values (615)304130.0%
 
ValueCountFrequency (%) 
0.000419982< 0.1%
 
0.000424461< 0.1%
 
0.000462371< 0.1%
 
0.000552853< 0.1%
 
0.000558752< 0.1%
 
0.00056161< 0.1%
 
0.000561811< 0.1%
 
0.000569021< 0.1%
 
0.000579791< 0.1%
 
0.000583611< 0.1%
 
ValueCountFrequency (%) 
0.9999960.1%
 
0.99998981.0%
 
0.999972602.6%
 
0.999961831.8%
 
0.999952652.6%
 
0.999945515.4%
 
0.999933943.9%
 
0.999922212.2%
 
0.999913413.4%
 
0.99994524.5%
 

Interactions

2020-11-29T21:43:51.045255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.173671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.283631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.394843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.499811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.609555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.722151image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.833729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:51.948244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.058279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.173079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.287718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.401654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.514274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.629970image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.740956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.856963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:52.973659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.080544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.181866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.287138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.500973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.606419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.713248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.819999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:53.930655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.035203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.144684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.254088image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.363467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.469861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.579410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.685195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.795065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:54.906023image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.015232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.119514image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.224500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.325633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.432165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.540516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.648725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.760510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.867282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:55.979813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.093401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.206411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.315171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.427173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.535627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.648667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.761879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.863273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:56.961001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.061310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.303074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.404591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.510672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.614614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.721265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.823579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:57.930297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.039250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.146285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.249877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.355331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.456447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.564582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.674316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.783587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.888846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:58.996240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.099443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.208042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.317630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.427640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.541095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.649168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.761646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.874044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:43:59.986996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.100407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.212990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.320826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.435416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.550166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.661349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.768408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.877679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:00.983172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:01.094010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:01.205976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:01.317983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:01.433154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:01.555672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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Correlations

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Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
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Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-29T21:44:34.667827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-11-29T21:44:34.916371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-11-29T21:44:25.673936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-29T21:44:26.111732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

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Last rows

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